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Nonlinear identification using adaptive local linear neuro-fuzzy

Autor Bagher Jamali, Hooshang Jazayeri-Rad, Mohammad Ali Ghayyem
en Limba Engleză Paperback – 12 iun 2012
Neural networks and fuzzy logic have some common features such as distributed representation of knowledge, ability to handle data with uncertainty and imprecision etc. Fuzzy logic has tolerance for imprecision of data, while neural networks have tolerance for noisy data. A neural network's learning capability provides a good way to adjust expert's knowledge and it automatically generates additional fuzzy rules and membership functions to meet certain specifications. This reduces the design time and cost. On the other hand, the fuzzy logic approach enhances the generalization capability of a neural network by providing more reliable output when extrapolation is needed beyond the limits of the training data. To simplify the design and optimization of fuzzy models, process learning techniques derived from neural networks (so called neuro-fuzzy approaches) can be used.Different architectures of neuro-fuzzy systems have been discussed by a number of researchers. In this research, neuro-fuzzy system networks are deployed and used for the dynamic modeling of a nonlinear MIMO system (heat recovery steam generator (HRSG)) and a permeability prediction process.
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Specificații

ISBN-13: 9783848411177
ISBN-10: 3848411172
Pagini: 108
Dimensiuni: 152 x 229 x 7 mm
Greutate: 0.17 kg
Editura: LAP LAMBERT ACADEMIC PUBLISHING AG & CO KG
Colecția LAP Lambert Academic Publishing

Notă biografică

Mr. Jamali is a graduate at the Petroleum University of Technology where he serves as Instructor in Control Lab and Commissioning of the Electronic Lab. He is an expert in process control, identification and artificial intelligence, which are the subjects he researches at PUT. He is the author of numerous publications on Nonlinear Identification.